Exploring real-time super-resolution generative adversarial networks

Abstract

Image super-resolution is an essential technology for improving user quality of experience of internet videos. As the state-of-the-art deep learning-based super-resolution technology, the enhanced super-resolution generative adversarial networks (ESRGAN) has the best performance in the perceptual quality of reconstructed images, and the efficient sub-pixel convolutional neural network (ESPCN) has the best real-time performance. This work proposes real-time super-resolution generative adversarial network (RTSRGAN). RTSRGAN takes the advantages of ESRGAN and ESPCN so as to simultaneously satisfy the demands on the real-time performance and the resulting pleasant artifacts of super-resolution at the client side. Our experimental studies demonstrate our proposed RTSRGAN can be used for super-resolution at the client side to enhance the real-time performance as well as ensure the image perceptual quality.We also find that RTSRGAN is suitable for restoring imageswith regularly changing texture featureswithout requiring training for individual image categories.